Multiple-trait genetic evaluation using genomic matrix

Abstract

Accuracy of the multiple-trait genetic evaluation based on genomic matrix obtained from allelic relationships was estimated through computer simulation and was compared with the accuracy of traditional Best Linear Unbiased Prediction (BLUP). Firstly, a base population (Ne = 100) was simulated and for each animal in the base population, three chromosomes were created. On each chromosome, 200 markers and 50 quantitative trait loci (QTLs) were randomly located. After 110 generations of random mating, linkage disequilibrium was created between the marker and QTL. Multiple-trait evaluation was done for two traits with high (h² = 0.46) and low heritability (h² = 0.1). In the first trial, in order to study the changes of evaluation accuracy along generations, after creating linkage disequilibrium, the population size of the last generation was expanded 3 times and random mating was done for the next three generations. Then, phenotypic and genotypic records of females for the last three generations were simulated. The results showed that the accuracy of evaluation  increased with an increase in the number of generations that make up the phenotypic and genotypic information. In the second trial, the studied methods were compared in an evaluation of progeny without phenotypic. For this purpose, animals of the last three generations (training set) were considered as parents, while with phenotypic and genotypic information, animals of generation 4 (validation set) were considered as progeny. These progenies were found in the genotypic information that was used to determine the allelic relationships, but were not found in the phenotypic information. Therefore, the use of their parents’ phenotypic information was evaluated. Using genomic matrices, the accuracy of evaluation increased. Average accuracy of evaluation for each trial was estimated based on 10 iterations, while statistical comparison was performed using student-t test. A significant difference was observed between the evaluation accuracy of the two studied methods.

Key words: Allelic relationships, multiple-trait evaluation, training set, validation set.